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检索条件"任意字段=3rd IEEE Intelligent Data Acquisition and Advanced Computing Systems"

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Research on intelligent Scoring Algorithm for English Writing Quality Based on Machine Learning

Research on Intelligent Scoring Algorithm for English Writin...

引用

Electronic Communications, Internet of Things and Big data (ICEIB), International Conference on

作者： Miao Yu Harbin Normal University Harbin China

The main writing characteristics of advanced learners in sentences are studied using the complexity analyzer of two sentence method, random forest, and logical regression machine learning algorithm. The results show that the accuracy of scoring and detection of the high-level composition is high even with the complexity of the two-sentence method, while the writing texts of advanced learners are outstanding in terms of unit length, lexical complexity, and specific sentence structure. The five most prominent syntactic features are average sentence length, the average number of sub-words contained in each word, the average total length of sub-words, the number of complex verb phrases in each subject and subordinate word, and the average number of complex noun phrases in each subword. The research result can be used as a reference for improving English composition learning ability and machine evaluation systems of composition.

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Development of intelligent Fault Early Warning System for Substation Integrating Satellite Remote Sensing and Ground Monitoring

Development of Intelligent Fault Early Warning System for Su...

引用

data Analytics, computing and Artificial Intelligence (ICDACAI), International Conference on

作者： Yuewei Tian Wenju Dai Xiaoyao Wu Lintao Shi Yongqiang Zhao Wenqiang Zou Guiyang Power Supply Bureau of Guizhou Power Grid Co. Ltd. Guiyang China China Southern Power Grid Technology Co. Ltd. Guangzhou China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

In this paper, an intelligent fault early warning system of substation integrating satellite remote sensing and ground monitoring technology is proposed and developed, aiming at improving the safety and stability of substation operation. The system architecture is designed as four layers, including data acquisition layer, data processing and fusion layer, fault early warning layer and user interaction layer. The data acquisition layer is responsible for collecting satellite remote sensing images and ground monitoring data; The data processing and fusion layer preprocesses, extracts and fuses the original data to improve the data quality. Fault early warning layer uses machine learning algorithm to build fault early warning model, and automatically identifies and predicts potential faults; The user interaction layer displays the early warning information in real time through the graphical interface, and provides visual fault analysis results. The fault early warning model based on ensemble learning adopts random forest algorithm and introduces weighted voting mechanism, which improves the accuracy and stability of prediction. Case analysis shows that the system has significantly improved the accuracy and timeliness of fault early warning in practical application, from 85.234% to 96.789%. The research results show that the intelligent fault early warning system of substation integrating satellite remote sensing and ground monitoring can overcome the limitations of traditional technology, realize comprehensive, real-time monitoring and efficient early warning of substation operation state, and provide strong support for the stable operation of power system.

关键词： Substations Satellites Machine learning algorithms Power system stability Alarm systems Predictive models Stability analysis Real-time systems Remote sensing Monitoring

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Smart Grid vs. intelligent Grid;Artificial Intelligence Takes the Power System to the Next Level

Smart Grid vs. Intelligent Grid;Artificial Intelligence Take...

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Digital Twins and Parallel Intelligence (DTPI), ieee International Conference on

作者： Sahand Liasi Narges S. Ghiasi Ramtin Hadidi Holcombe Department of Electrical and Computer Engineering Clemson University Charleston SC USA

This paper discusses the concept of intelligent power systems, particularly the distinction between smart grids and intelligent grids. Smart grids represent a convergence of various energy technologies and communication systems, enabling multidomain interactions. However, in such a grid, humans are still the ultimate decision maker based on the gathered information. By adding artificial intelligence to the system, smart grids can be upgraded to intelligent grids. This article explores the cutting-edge methods and propositions of machine learning and artificial intelligence, and their influence on a plethora of smart grid applications, aiding their transition to truly intelligent grids. The piece highlights the surge in interest and rapid advancement in employing machine learning and AI techniques to effectively tackle technical obstacles across various facets of the smart grid. However, it notes unresolved issues meriting additional research, such as high-performance data processing for informed decision-making in large, intricate multi-energy systems, and lightweight solutions based on machine learning. Additionally, the article spotlights the potential of harnessing progressive computing and communication technologies such as edge computing, pervasive internet of things, and 5G wireless networks within the smart grid framework. Simulating smart grids requires overcoming challenges posed by the integration of diverse subsystems with varying time scales. Co-simulation is employed for these cases. This paper presents the state-of-the-art in simulating intelligent power systems and addresses challenges in applying co-simulation to test cases spanning various domains, including information and communication technology (ICT) and power systems. We anticipate that this study will serve as a launching pad for continued exploration and advancement in this burgeoning area of knowledge and practice.

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Research on Scenario Analysis in Aircraft E-Service 3

Research on Scenario Analysis in Aircraft E-Service

引用

3rd ieee International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2021

作者： Wang, Huili Chen, Xiao Li, Siyu Engineering Data Department Shanghai Aircraft Customer Service Co. Ltd. Shanghai China

ISBN: (纸本)9781665425186

advanced aircraft manufactures are taking digital operation support as their main objective to providing value-added services for airlines during aircraft lifecycle. This paper puts forward a concept of aircraft e-Service, which aims at providing aircraft operation support environment with "data interconnection, systems integration, resources dynamic allocation, people coordination, service digitalization and networking, and intelligent decision and analysis", and then improving operation efficiency of airlines. E-Service is the integration of several different systems, so system engineering approaches will be mainly used. This paper focuses on the scenario analysis method, selecting several typical e-Service scenarios and implementing the corresponding scenario analysis and functional analysis, which provided a research methodology for e-Service full system research and development. © 2021 ieee

关键词： Functional analysis

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advanced Modeling of Sodium-Ion Batteries for Electric Vehicles: Enabling Wireless Communication and intelligent Transportation systems

Advanced Modeling of Sodium-Ion Batteries for Electric Vehic...

引用

Wireless Africa Conference (WAC)

作者： Khanyisile Masemola Cuthbert Nyamupangedengu David G. Dorrell School of Electrical and Information Engineering University of the Witwatersrand Johannesburg South Africa Department of Mechanical & Material Engineering University of Turku Turku Finland

ISBN: (数字)9798331517588

ISBN: (纸本)9798331517595

Sodium-ion batteries (SIBs) have emerged as a viable substitute for lithium-ion batteries (LIBs) in electric vehicles (EVs) due to the growing need for sustainable energy solutions. A comprehensive modeling framework for SIBs designed especially for electric vehicle applications is presented in this paper, with a focus on how these batteries integrate with wireless communication and intelligent transportation systems (ITS). The thermal and electrochemical models of SIBs, their possible use in vehicle-to-everything (V2X) systems, and the wireless state-of-charge (SoC) monitoring via real-time communication protocols are investigated. Simulation results demonstrated the efficiency of SIBs in vehicular applications, their compatibility with wireless data transfer protocols, and their contribution to energy optimization in ITS. The paper concludes with insights into the future of SIBs in EVs and wireless transportation ecosystems.

关键词： Wireless sensor networks Protocols Simulation Transportation Real-time systems Batteries Dedicated short range communication Vehicle-to-everything Optimization Testing

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Malware analysis and multi-label category detection issues: Ensemble-based approaches 3

Malware analysis and multi-label category detection issues: ...

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3rd International Conference on intelligent data Science Technologies and Applications, IDSTA 2022

作者： Alsmadi, Izzat Al-Ahmad, Bilal Alsmadi, Mohammad Department of Computing and Cyber Security Texas A&m San Antonio United States The University of Jordan Computer Information Systems Department Aqaba Jordan Applied Science University Information Technology Amman Jordan

ISBN: (数字)9781665499606

ISBN: (纸本)9781665499606

Detection of malware and security attacks is a complex process that can vary in its details and analysis activities. As part of the detection process, malware scanners try to categorize a malware once it is detected under one of the known malware categories (e.g. worms, spywares, viruses, etc.). However, many studies and researches indicate problems with scanners categorizing or identifying a particular malware under more than one malware category. This paper, and several others, show that machine learning can be used for malware detection especially with ensemble base prediction methods. In this paper, we evaluated several custom-built ensemble models. We focused on multi-label malware classification as individual or classical classifiers showed low accuracy in such *** paper showed that recent machine models such as ensemble and deep learning can be used for malware detection with better performance in comparison with classical models. This is very critical in such a dynamic and yet important detection systems where challenges such as the detection of unknown or zero-day malware will continue to exist and evolve. © 2022 ieee.

关键词： Scanning

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Industrial Control intelligent Operation and Maintenance System Based on Zigbee Wireless Communication Technology 3

Industrial Control Intelligent Operation and Maintenance Sys...

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3rd International Conference on Artificial Intelligence and advanced Manufacture, AIAM 2021

作者： Fan, Jiaqi College of Electronic and Information Southwest Minzu University Sichuan Chengdu China

ISBN: (纸本)9781665417327

In order to solve a series of problems of the existing industrial control equipment based on the traditional operation and maintenance system, based on Zigbee networking technology, artificial intelligence and heartbeat detection technology, a set of intelligent operation and maintenance management system of automated industrial control system has been constructed. Through the mutual detection of equipment network and hardware, data analysis and machine learning, the shortcomings of traditional operation and maintenance systems are supplemented, and the cost of manpower and material resources for maintaining equipment and testing equipment operation status is reduced. Improve the reliability of industrial automation operation and maintenance. © 2021 ieee.

关键词： Zigbee

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Product Information Visualization systems in the Era of Intelligence 3

Product Information Visualization Systems in the Era of Inte...

引用

3rd International Conference on Artificial Intelligence and advanced Manufacture, AIAM 2021

作者： Li, Xinwei Qu, Muzi Luxun Academy of Fine Arts Shenyang China

ISBN: (纸本)9781665417327

In the era of artificial intelligence and big data, visualization is an inevitable trend for product information expression. To transmit comprehensive, accurate, efficient, and clear product information, designers need to leverage information technologies such as big data analytics, image recognition and natural language processing to enrich product information visualization. This paper discussed the key practices when utilizing big data and artificial intelligence in product information design and proposed a methodology of product information visualization. Through qualitative analysis, interdisciplinary and comparative research methods, intelligent data information is implemented in human-computer interaction experience. It is an urgent development problem of the integration of design and technology in the AI's times. © 2021 ieee.

关键词： Visualization

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No One Acts like You: AI based Behavioral Biometric Identification 3

No One Acts like You: AI based Behavioral Biometric Identifi...

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3rd International Conference on Next Generation computing Applications, NextComp 2022

作者： Rub, Matthias Herbst, Jan Lipps, Christoph Schotten, Hans Dieter Intelligent Networks Research Group German Research Center for Artificial Intelligence KaiserslauternD-67663 Germany Institute for Wireless Communication and Navigation University of Kaiserslautern Germany

ISBN: (数字)9781665469548

ISBN: (纸本)9781665469548

There has already been a significant increase in Mobile Broadband (MBB) subscribers over the past few years, which is expected to increase further as a result of the current trend towards the Sixth Generation (6G) wireless systems. This is accompanied by a growing need for confidentiality, integrity and undoubtedly authentication of the communication units involved (human and machine). In particular, authentication is challenging with respect to human participants, as often only possession of a token or knowledge of a code/password is requested, which validates the underlying claim: who is the person. Furthermore, as knowledge-based passwords and object-based keys can be lost or stolen, biometric-based approaches rely on traits, which are intrinsically connected to one person. Therefore this work demonstrates the feasibility of Long Short-Term Memories (LSTMs) - a type of Neural Network (NN)-, to identify and authenticate people by their characteristic arm movements during specific everyday tasks. For measurements of the arm movement a wrist-band wearable has been developed. The focus of the evaluation with Deep Neural Networks (DNN) is on different high level structures of NNs and data. Multiple NNs are allocated to either different test subjects and user-activities. In a first task 1 out of n people had to be identified with a maximum average precision of 92.0 %. In a second evaluation the Networks performed in a n out of m authentication with an maximum average precision of 81.4 %. With those results the potential of NNs for biometric authentication with behavioral sensors is demonstrated. Additionally in view of the necessary personal biometric data, ethical and legal aspects are highlighted. © 2022 ieee.

关键词： Biometrics

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A Comprehensive Insight on Machine Learning Enabled Internet of Things Recommender systems (IoTRS) 3

A Comprehensive Insight on Machine Learning Enabled Internet...

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3rd International Conference on Advances in computing, Communication Control and Networking, ICAC3N 2021

作者： Ahlawat, Partibha Rana, Chhavi University Institute of Engineering and Technology Department of Computer Science and Engineering Maharishi Dayanand University Rohtak India

ISBN: (纸本)9781665438117

With the advancement in technologies, the opportunities for new integrated solutions are also unlocked. Internet of Things by connecting cyber and physical world introduce enormous paradigm change in computing and information technologies. Recommender system helps in filtering the data by taking care of inclinations of users to prevail the issues related to overdosing of IoT generated data. Machine learning techniques can be applied for producing intelligent recommendation by exploiting IoT-based dynamic, scalable and heterogeneous data. This paper reviews the machine learning techniques applied for generating personalized recommendation in IoT-based services and give the brief introduction of IoT recommender systems and machine learning techniques. Personalization and recommendations services in different application domains are enhanced by the incorporation of machine learning algorithms. In IoT recommendations machine learning also leaving their remarkable presence with integrating automaticity and dynamicity. © 2021 ieee.

关键词： Internet of things

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